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Best fit

Who this works for.

  • Computer vision in production
  • Recommendation systems and ranking
  • Forecasting and time-series
What's included

Every Australian engagement comes with these foundations.

Problem framing

Half the work is turning a business problem into a learnable one. We do that with you.

Data audit

Honest assessment of whether your data supports the model you want.

Training pipeline

Reproducible training, version-controlled datasets, model registry.

Production serving

Inference optimised for latency and cost, with proper monitoring.

MLOps

Retraining schedules, drift detection, A/B testing of models.

Process

How a machine learning engagement actually runs.

01

Discovery

Define the problem, audit the data, set success metrics.

02

Baseline

Build the simplest possible model first. Often it's enough.

03

Iterate

Improve through better features, better data, or better models, in that order.

04

Deploy

Production serving with monitoring and drift detection.

What you walk away with

Deliverables.

  • Production model with API
  • Training pipeline
  • Eval suite
  • Monitoring dashboard
  • Model card and documentation
Stack

Tools we use.

Python PyTorch Scikit-learn Hugging Face Modal Weights & Biases BentoML

We're stack-flexible. If your team already runs on something different, we'll match it.

Australian pricing

Three engagement sizes. One fixed price for each.

Quotes from three matched Australian developers come with their own pricing in AUD. These ranges are what most projects land on.

Spike
from $3.5k

2-week feasibility study with a baseline model on your data.

  • Data audit
  • Baseline model
  • Go/no-go report
Most picked
Production model
from $16k

Production-ready model deployed and monitored.

  • Custom training pipeline
  • Production serving
  • MLOps
  • 8 to 12 weeks
ML platform
from $42k

Multi-model platform with shared infra, feature store, and tooling.

  • Feature store
  • Multiple models
  • Internal platform
  • Embedded team
FAQ

Common questions.

LLMs are great for unstructured text and one-off tasks. Custom ML wins for high-volume scoring, vision, and tasks where accuracy beats flexibility.
Depends entirely on the problem. We tell you honestly after the data audit, sometimes the answer is more than you have.
Ready when you are

Compare three machine learning developers in Australia. Free.

Two-minute brief. Three tailored quotes within 24 hours. No pressure, no obligation, no spam.

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